ON Combined_psuedobulk anlysis
Katie Emberley
26 February 2025
1 Load Packages
if (!require("knitr")) {install.packages("knitr"); require("knitr")}
if (!requireNamespace('BiocManager', quietly = TRUE)) {install.packages('BiocManager'); require("BiocManager")}
if (!require("dplyr")) {install.packages("dplyr"); require("dplyr")}
if (!require("stringr")) {install.packages("stringr"); require("stringr")}
if (!require("Seurat")) {install.packages("Seurat"); require("Seurat")}
if (!require("sctransform")) {install.packages("sctransform"); require("sctransform")}
if (!require("glmGamPoi")) {BiocManager::install('glmGamPoi'); require("glmGamPoi")}
if (!require("patchwork")) {install.packages("patchwork"); require("patchwork")}
if (!require("ggplot2")) {install.packages("ggplot2"); require("ggplot2")}
if (!require("EnhancedVolcano")) {BiocManager::install('EnhancedVolcano'); require("EnhancedVolcano")}
if (!require("DESeq2")) {BiocManager::install('DESeq2'); require("DESeq2")}
if (!require("tidyverse")) {install.packages("tidyverse"); require("tidyverse")}
if (!require("RColorBrewer")) {install.packages("RColorBrewer"); require("RColorBrewer")}
if (!require("car")) {install.packages("car"); require("car")}
if (!require("openxlsx")) {install.packages("openxlsx"); require("openxlsx")}
if (!require("readxl")) {install.packages("readxl"); require("readxl")}
if (!require("ggrepel")) {install.packages("ggrepel"); require("ggrepel")}
if (!require("gghighlight")) {install.packages("gghighlight"); require("gghighlight")}
if (!require("ggpmisc")) {install.packages("ggpmisc"); require("ggpmisc")}
if (!require("data.table")) {install.packages("data.table"); require("data.table")}
if (!require("here")) {install.packages("here"); require("here")}
if (!require("NatParksPalettes")) {install.packages("NatParksPalettes"); require("NatParksPalettes")}
if (!require("svglite")) {install.packages("svglite"); require("svglite")}
if (!require("ggvenn")) {install.packages("ggvenn"); require("ggvenn")}
if (!require("kableExtra")) {install.packages("kableExtra"); require("kableExtra")} # for color brewer
here()## [1] "/Users/emberley/Documents/PIPseq/LIB241121BE/R files"2 Helpful Objects
# Color palette for PIPseq that coordinates with Yellowstone for clusters
cluster_colors<- c("#0067A2", "#CC782B", "#5A8D66", "#509EA0", "#71A2B8", "#8A9BA9", "#B46DB3", "black")
# Color palette for replicates -- adapted from the Torres colors
replicate_colors <- c( "#7391BD" ,"#894846" ,"#E9988C" ,"#535260", "#B7A7A6" ,"#785838", "#C68D61" ,"#93995C")
# Color pallete for "disordered" sample_ID (in order of lexicon, 1, 10-16, 2-9)
sample_colors_d <- c("#C00000", "#FF8082", "#BFA85F", "#CCCA79", "#799E51", "#448A64",
"#568BB1", "#775790", "#FF0000", "#FFC000", "#F5EF1E", "#92D050",
"#00B050", "#0070C0", "#7030A0", "#D08B8C")
# Group color pallet
age_colors <- c("#BFA85F", "purple")
# genotype color pallet
genotype_colors <- c("orange", "blue")3 Increase Globals
options(future.globals.maxSize = 74 * 1024^3) # 55 GB
getOption("future.globals.maxSize") #59055800320## [1] 794568949764 Load Object
5 Psuedobulk Object
5.1 Group the cells and aggregate
Group by the cell type and samples
5.2 Extract the counts
We will get a huge matrix with columns as the cell_samples and rows as genes
## 16 x 16 sparse Matrix of class "dgCMatrix"##
## Xkr4 1.005065e+04 8531.7380157 28428.886737 10604.305657 27195.171305
## Gm1992 3.513907e+01 28.8691427 318.077866 69.204201 66.358061
## Gm19938 9.863506e+02 763.3925108 2943.304557 1162.562618 2349.565503
## Gm37381 8.609018e+01 44.3063990 90.245531 79.863555 93.681319
## Rp1 2.107700e+02 209.2694956 264.376969 342.601798 310.857194
## Sox17 1.398350e+01 10.8556093 22.217328 . 16.000000
## Gm37587 9.755971e-01 1.0000000 2.000000 1.909418 .
## Gm37323 . . 3.000000 . 8.000000
## Mrpl15 3.440870e+02 659.7764855 1292.785611 861.924570 1169.309141
## Lypla1 1.429060e+03 2108.9332342 3418.550371 2491.205507 4511.441558
## Tcea1 2.190187e+03 3266.8279613 5966.326153 3865.592403 6579.825801
## Rgs20 5.308359e+03 8267.0689440 9220.293812 6291.602598 28530.719980
## Gm16041 1.000000e+00 0.9848024 4.000000 1.000000 3.000000
## Atp6v1h 1.720426e+03 2684.6824962 6535.562807 4591.969173 6627.277939
## Oprk1 1.976713e+00 9.8252999 5.227619 2.348087 1.785231
## Npbwr1 . . . 1.000000 1.000000
##
## Xkr4 1.429128e+04 2.221777e+04 35199.592124 7899.357666 7985.941494
## Gm1992 4.176587e+01 6.013632e+02 322.646652 23.794623 73.545658
## Gm19938 1.337453e+03 2.187208e+03 3721.558003 797.973781 839.914269
## Gm37381 1.082198e+02 7.468100e+01 250.442379 79.223174 59.028343
## Rp1 3.411909e+02 3.164240e+02 2040.391228 198.240008 104.395270
## Sox17 7.370645e+01 1.731185e+02 32.747574 16.000000 4.000000
## Gm37587 3.000000e+00 3.988385e+00 1.872795 1.000000 1.000000
## Gm37323 2.879465e+00 2.000000e+00 4.000000 2.000000 .
## Mrpl15 7.694570e+02 1.011745e+03 1044.521467 284.746009 264.083560
## Lypla1 2.814141e+03 3.543669e+03 3427.365583 1411.607888 906.369981
## Tcea1 3.989301e+03 5.456729e+03 4930.747053 1898.709044 1415.798304
## Rgs20 1.191737e+04 1.044019e+04 6428.747631 3225.760061 2477.942463
## Gm16041 9.815164e-01 9.164695e-01 1.000000 . .
## Atp6v1h 4.310653e+03 5.418279e+03 6693.811154 1953.660254 1498.061720
## Oprk1 4.849232e+00 4.903818e+00 5.454435 2.827486 1.974671
## Npbwr1 1.965453e+00 2.925962e+00 . . .
##
## Xkr4 4617.3599233 5416.475418 6444.1838137 8735.2728291 8770.040917
## Gm1992 19.9922997 20.674153 30.1033969 159.0593248 99.398404
## Gm19938 493.5839763 449.720121 679.1159401 767.9078366 1034.660216
## Gm37381 63.5715425 61.499140 70.0959505 61.5985225 95.941301
## Rp1 159.1010489 123.826772 177.6726989 176.5307113 568.234546
## Sox17 . 9.980129 0.9870518 7.0000000 4.995495
## Gm37587 0.9890630 . 2.0000000 1.0000000 2.000000
## Gm37323 1.0000000 2.000000 1.0000000 0.9866936 .
## Mrpl15 314.0395904 236.280570 330.7964683 307.1836364 261.155023
## Lypla1 1003.6451369 877.646450 1275.5648670 1049.9732704 908.769311
## Tcea1 1475.2332515 1355.550575 1939.2613952 1685.3659795 1303.841173
## Rgs20 1943.0127646 3939.503522 3227.6199132 3018.8145235 1613.205682
## Gm16041 . . . 1.0000000 .
## Atp6v1h 1947.4733537 1218.252027 1859.9208987 1597.0949452 1623.701798
## Oprk1 0.9860042 3.986753 0.9378364 4.9447968 .
## Npbwr1 . . . . .
##
## Xkr4 1.309683e+04
## Gm1992 3.609525e+01
## Gm19938 1.299005e+03
## Gm37381 6.380994e+01
## Rp1 2.434054e+02
## Sox17 1.095859e+01
## Gm37587 .
## Gm37323 5.000000e+00
## Mrpl15 6.410937e+02
## Lypla1 2.644889e+03
## Tcea1 3.985017e+03
## Rgs20 1.462818e+04
## Gm16041 2.000000e+00
## Atp6v1h 2.516210e+03
## Oprk1 1.232708e-01
## Npbwr1 9.606854e-01## [1] "g1-yWT10-a" "g10-aWT10-b" "g11-y10-b" "g12-a10-b" "g13-yWT20-b"
## [6] "g14-aWT20-b" "g15-y20-b" "g16-a20-b" "g2-aWT10-a" "g3-y10-a"
## [11] "g4-a10-a" "g5-yWT20-a" "g6-aWT20-a" "g7-y20-a" "g8-a20-a"
## [16] "g9-yWT10-b"5.3 Add Metadata
This allows you to add whichever components of metadata you’d like to include to graph you PCA data by and whichever components you want to design the DESeq object from.
metadata <- seu.obj@meta.data %>%
as.data.frame() %>%
dplyr::select(sample_ID, replicate, age_group, genotype, day_nuclei_isolated) # CHANGE These correspond to the column names of the metadata you want to extract
dim(metadata)## [1] 118550 5## [1] 16 5# Rename rows to represent the sample IDs rather than the cell barcode IDs
rownames(metadata) <- metadata$sample_ID
head(metadata)# This enusres the columns match up between pbo and the metadata.
sample_names <- unique(seu.obj@meta.data$sample_ID) # CHANGE to be the column that you have for your samples
sample_names## [1] "1_yWT10_a" "9_yWT10_b" "2_aWT10_a" "10_aWT10_b" "3_y10_a"
## [6] "11_y10_b" "4_a10_a" "12_a10_b" "5_yWT20_a" "13_yWT20_b"
## [11] "6_aWT20_a" "14_aWT20_b" "7_y20_a" "15_y20_b" "8_a20_a"
## [16] "16_a20_b"5.4 Make DESeq Dataset
When making the DESeq dataset, make sure the countData have the exact
same column names in the exacts same order as the rows of metadata. You
get to choose your design for what the model will base it’s
sample matrix on. Select one or more of your metadata columns.
dds <- DESeqDataSetFromMatrix(countData = round(pbo), # Integers must be used for this matrix.
colData = metadata,
design = ~ sample_ID) # CHANGE I am choosing to use sample ID rather than replicate here as we are trying to identify whether or not y20icKO batch differences will be an issue.## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors5.5 Run PCA analyses
5.5.1 Plot PCA analyses
Change this section to customize based on which metadata you would like plotted. Note, this section includes several customizations that are user specific based on metadata and color preferences.
sample_names <- unique(rld$sample_ID)
# Plot PCA
sample<-DESeq2::plotPCA(rld, ntop = 1000, intgroup = "sample_ID") +
scale_color_manual(values = sample_colors_d)+
geom_text_repel(aes(label = sample_names))+
coord_fixed(ratio = 2.5) + # Aspect ratio of 2 means the x-axis is twice as long as the y-axis
theme_minimal()## using ntop=1000 top features by variance## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.replicate<-DESeq2::plotPCA(rld, ntop = 1000, intgroup = "replicate") +
scale_color_manual(values = replicate_colors)+
coord_fixed(ratio = 2.5) + # Aspect ratio of 2 means the x-axis is twice as long as the y-axis
theme_minimal()## using ntop=1000 top features by variance
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.age<-DESeq2::plotPCA(rld, ntop = 1000, intgroup = "age_group") +
scale_color_manual(values = age_colors)+
coord_fixed(ratio = 2.5) + # Aspect ratio of 2 means the x-axis is twice as long as the y-axis
theme_minimal()## using ntop=1000 top features by variance
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.genotype<-DESeq2::plotPCA(rld, ntop = 1000, intgroup = "genotype") +
scale_color_manual(values = genotype_colors)+
coord_fixed(ratio = 2.5) + # Aspect ratio of 2 means the x-axis is twice as long as the y-axis
theme_minimal()## using ntop=1000 top features by variance
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.batch<-DESeq2::plotPCA(rld, ntop = 1000, intgroup = "day_nuclei_isolated") +
scale_color_manual(values = c("pink", "darkgreen"))+
coord_fixed(ratio = 2.5) + # Aspect ratio of 2 means the x-axis is twice as long as the y-axis
theme_minimal()## using ntop=1000 top features by variance
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Save the plots
folder<-here("4-Analysis", "PCA")
ggsave("PCA by Sample.svg", sample, path = folder)## Saving 7 x 5 in image## Saving 7 x 5 in image## Saving 7 x 5 in image## Saving 7 x 5 in image## Saving 7 x 5 in image6 Run PCA on a per cluster basis
7 Psuedobulk Object
7.1 Group the cells and aggregate
Group by the cell type and samples
7.2 Extract the counts
We will get a huge matrix with columns as the cell_samples and rows as genes
## 16 x 16 sparse Matrix of class "dgCMatrix"##
## Xkr4 1093.643289 2125.9808528 5571.40110 2910.169931 3766.602304
## Gm1992 3.877677 . 19.45336 4.200044 5.408629
## Gm19938 111.156146 216.8727370 598.49970 308.845365 393.883635
## Gm37381 4.846212 4.8585340 10.48722 7.847197 1.918910
## Rp1 6.569944 8.3600801 18.91743 19.067562 9.648479
## Sox17 . . . . .
## Gm37587 . . . . .
## Gm37323 . . . . .
## Mrpl15 14.611873 40.7274177 98.04053 45.053542 40.795150
## Lypla1 44.798776 106.5570993 307.61697 165.623627 165.189108
## Tcea1 89.488122 161.2983765 506.50764 280.259347 219.548140
## Rgs20 424.999744 830.0713778 2295.28198 1721.041622 1339.272437
## Gm16041 . . . . .
## Atp6v1h 65.013997 145.2898776 526.59777 249.748189 296.068460
## Oprk1 . 0.9397222 . . .
## Npbwr1 . . . . .
##
## Xkr4 2730.3073634 2702.2570366 3306.953565 1181.6350574 1202.997635
## Gm1992 2.7271025 4.8565451 7.025408 0.9276348 5.830499
## Gm19938 290.5129972 289.6803223 350.299947 127.3282374 154.016181
## Gm37381 7.6221420 2.4827228 5.935597 5.8677618 4.866207
## Rp1 9.5649033 13.7432519 15.844332 5.3997943 10.416664
## Sox17 0.9370237 . . . .
## Gm37587 . . . . .
## Gm37323 . . . . .
## Mrpl15 29.7736864 46.2695788 69.347516 8.6552010 10.513704
## Lypla1 135.5127066 115.5226059 155.107204 71.9869395 45.342514
## Tcea1 181.1124276 168.9494111 269.699791 84.4621687 116.605616
## Rgs20 1012.5453157 902.7047437 1281.164265 472.4927685 483.700764
## Gm16041 . . . . .
## Atp6v1h 194.8605804 235.4907587 354.307211 117.7060163 124.741939
## Oprk1 . 0.6732986 . . .
## Npbwr1 . . . . .
##
## Xkr4 1484.828910 613.6564737 899.021739 807.462087 877.860815 1609.6961828
## Gm1992 1.943508 . 1.951583 . 1.936774 4.9111963
## Gm19938 147.332860 66.7199297 102.629435 89.410523 95.282660 199.6115093
## Gm37381 8.831114 2.9294703 4.909370 3.939778 3.794515 0.8046319
## Rp1 9.250730 4.7585909 2.863175 4.633544 3.380324 16.2984510
## Sox17 . . . . . .
## Gm37587 . . . . 1.000000 .
## Gm37323 . . . . . .
## Mrpl15 34.386934 15.8584266 11.722197 5.831669 18.723732 18.1360671
## Lypla1 90.293666 21.6056832 36.373197 35.047288 44.998423 89.5033630
## Tcea1 152.134523 48.3833076 67.031763 59.603066 74.745816 117.0104168
## Rgs20 897.275506 232.5197115 404.637173 280.463710 386.136917 604.2367263
## Gm16041 . . . . . .
## Atp6v1h 160.097635 65.0372443 65.495332 52.148529 82.758164 82.1776048
## Oprk1 . 0.9988837 . . . .
## Npbwr1 . . . . . .## [1] "OPC_1-yWT10-a" "OPC_10-aWT10-b" "OPC_11-y10-b" "OPC_12-a10-b"
## [5] "OPC_13-yWT20-b" "OPC_14-aWT20-b" "OPC_15-y20-b" "OPC_16-a20-b"
## [9] "OPC_2-aWT10-a" "OPC_3-y10-a" "OPC_4-a10-a" "OPC_5-yWT20-a"
## [13] "OPC_6-aWT20-a" "OPC_7-y20-a" "OPC_8-a20-a" "OPC_9-yWT10-b"7.2.1 Data transformation
- Transpose the columns and rows
- Convert to data.frame
Check the structure of the data frame
Split the rows (cell_samples) to remove the sample ID and only keep cell type information in the data frame - Then we will get a vector containing the cell types, which will be used to split the data frame as a factor variable
# matching anything that's after the '_' and replace it with nothing (removing it)
splitRows <- gsub('_.*', '', rownames(pbo.t))
splitRows## [1] "OPC" "OPC" "OPC" "OPC" "OPC"
## [6] "OPC" "OPC" "OPC" "OPC" "OPC"
## [11] "OPC" "OPC" "OPC" "OPC" "OPC"
## [16] "OPC" "Oligolineage" "Oligolineage" "Oligolineage" "Oligolineage"
## [21] "Oligolineage" "Oligolineage" "Oligolineage" "Oligolineage" "Oligolineage"
## [26] "Oligolineage" "Oligolineage" "Oligolineage" "Oligolineage" "Oligolineage"
## [31] "Oligolineage" "Oligolineage" "Microglia" "Microglia" "Microglia"
## [36] "Microglia" "Microglia" "Microglia" "Microglia" "Microglia"
## [41] "Microglia" "Microglia" "Microglia" "Microglia" "Microglia"
## [46] "Microglia" "Microglia" "Microglia" "Astrocyte" "Astrocyte"
## [51] "Astrocyte" "Astrocyte" "Astrocyte" "Astrocyte" "Astrocyte"
## [56] "Astrocyte" "Astrocyte" "Astrocyte" "Astrocyte" "Astrocyte"
## [61] "Astrocyte" "Astrocyte" "Astrocyte" "Astrocyte" "Vascular"
## [66] "Vascular" "Vascular" "Vascular" "Vascular" "Vascular"
## [71] "Vascular" "Vascular" "Vascular" "Vascular" "Vascular"
## [76] "Vascular" "Vascular" "Vascular" "Vascular" "Vascular"
## [81] "KOOL" "KOOL" "KOOL" "KOOL" "KOOL"
## [86] "KOOL" "KOOL" "KOOL" "KOOL" "KOOL"
## [91] "KOOL" "KOOL" "KOOL" "KOOL" "KOOL"Split data.frame using the splitRows vector - It will return a list with each element corresponding with a cell type - Having data in such a list object makes it convenient to fetch matrices for any cell type in the data
Check the matrix - using OPC as an example here
Fix colnames and transpose it back to columns as the cell_samples and rows as genes
# use 'lapply' to remove the cell type and only leave the sample ID, and also re-transpose the matrix
pbo.split.modified <- lapply(pbo.split, function(x){
rownames(x) <- gsub('.*_(.*)', '\\1', rownames(x)) # remove the cell type name from the row name
t(x)
})Check if you have performed the changes
## 1-yWT10-a 10-aWT10-b 11-y10-b 12-a10-b 13-yWT20-b 14-aWT20-b
## Xkr4 1093.643289 2125.980853 5571.40110 2910.169931 3766.602304 2730.3073634
## Gm1992 3.877677 0.000000 19.45336 4.200044 5.408629 2.7271025
## Gm19938 111.156146 216.872737 598.49970 308.845365 393.883635 290.5129972
## Gm37381 4.846212 4.858534 10.48722 7.847197 1.918910 7.6221420
## Rp1 6.569944 8.360080 18.91743 19.067562 9.648479 9.5649033
## Sox17 0.000000 0.000000 0.00000 0.000000 0.000000 0.9370237## 1-yWT10-a 10-aWT10-b 11-y10-b 12-a10-b 13-yWT20-b 14-aWT20-b
## Xkr4 8289.87520 5237.2574416 1.606653e+04 5360.84392 20147.50036 9807.792751
## Gm1992 17.90879 13.6822858 4.580000e+01 10.47649 32.95969 24.798255
## Gm19938 784.05682 431.2694088 1.612192e+03 607.25261 1643.64790 895.846572
## Gm37381 54.43715 15.0094934 3.193915e+01 13.87819 52.27220 51.836951
## Rp1 123.25262 70.4733464 6.223708e+01 148.36692 127.60106 95.831363
## Sox17 0.00000 0.9287855 6.012309e-01 0.00000 0.00000 1.889099# List of clusters
clusters <- c("OPC", "Oligolineage", "Microglia", "Astrocyte", "Vascular")
# Loop over each cluster
for (cluster in clusters) {
# Dynamically access the corresponding pbo.split.modified object (pbo.split.modified$<cluster>)
pbo <- pbo.split.modified[[cluster]]
# Ensure the columns of pbo match the sample names from your metadata
sample_names <- unique(seu.obj@meta.data$sample_ID)
colnames(pbo) <- sample_names
# Create DESeq2 dataset
dds <- DESeqDataSetFromMatrix(countData = round(pbo),
colData = metadata,
design = ~ sample_ID) # Change design formula if needed
# Run PCA (Rlog transformation)
rld <- rlog(dds, blind = TRUE)
# Plot PCA by different metadata (you can customize the plots)
sample_plot <- DESeq2::plotPCA(rld, ntop = 1000, intgroup = "sample_ID") +
scale_color_manual(values = sample_colors_d) +
geom_text_repel(aes(label = sample_names)) +
theme_minimal() +
ggtitle(paste(cluster, "- sample_plot"))
replicate_plot <- DESeq2::plotPCA(rld, ntop = 1000, intgroup = "replicate") +
scale_color_manual(values = replicate_colors) +
theme_minimal() +
ggtitle(paste(cluster, "- replicate_plot"))
age_plot <- DESeq2::plotPCA(rld, ntop = 1000, intgroup = "age_group") +
scale_color_manual(values = age_colors) +
theme_minimal() +
ggtitle(paste(cluster, "- age_plot"))
genotype_plot <- DESeq2::plotPCA(rld, ntop = 1000, intgroup = "genotype") +
scale_color_manual(values = genotype_colors) +
theme_minimal() +
ggtitle(paste(cluster, "- genotype_plot"))
batch_plot <- DESeq2::plotPCA(rld, ntop = 1000, intgroup = "day_nuclei_isolated") +
scale_color_manual(values = c("pink", "darkgreen")) +
theme_minimal() +
ggtitle(paste(cluster, "- batch_plot"))
# Print each plot to the console
plot(sample_plot)
plot(replicate_plot)
plot(age_plot)
plot(genotype_plot)
plot(batch_plot)
# Save the plots
folder <- here("4-Analysis", "PCA")
ggsave(paste0("PCA_", cluster, "_by_Sample.svg"), sample_plot, path = folder)
ggsave(paste0("PCA_", cluster, "_by_Replicate.svg"), replicate_plot, path = folder)
ggsave(paste0("PCA_", cluster, "_by_Age.svg"), age_plot, path = folder)
ggsave(paste0("PCA_", cluster, "_by_Genotype.svg"), genotype_plot, path = folder)
ggsave(paste0("PCA_", cluster, "_by_Batch.svg"), batch_plot, path = folder)
# Optionally, print the current cluster being processed
print(paste("Processing cluster:", cluster))
}## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## using ntop=1000 top features by variance## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## Saving 7 x 5 in image## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image## [1] "Processing cluster: OPC"## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image## [1] "Processing cluster: Oligolineage"## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image## [1] "Processing cluster: Microglia"## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image## [1] "Processing cluster: Astrocyte"## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## using ntop=1000 top features by variance
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image## [1] "Processing cluster: Vascular"8 Session Info
## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.2
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices datasets utils
## [8] methods base
##
## other attached packages:
## [1] kableExtra_1.4.0 ggvenn_0.1.10
## [3] svglite_2.1.3 NatParksPalettes_0.2.0
## [5] here_1.0.1 data.table_1.16.0
## [7] ggpmisc_0.6.0 ggpp_0.5.8-1
## [9] gghighlight_0.4.1 readxl_1.4.3
## [11] openxlsx_4.2.7 car_3.1-2
## [13] carData_3.0-5 RColorBrewer_1.1-3
## [15] lubridate_1.9.3 forcats_1.0.0
## [17] purrr_1.0.2 readr_2.1.5
## [19] tidyr_1.3.1 tibble_3.2.1
## [21] tidyverse_2.0.0 DESeq2_1.44.0
## [23] SummarizedExperiment_1.34.0 Biobase_2.64.0
## [25] MatrixGenerics_1.16.0 matrixStats_1.4.1
## [27] GenomicRanges_1.56.1 GenomeInfoDb_1.40.1
## [29] IRanges_2.38.1 S4Vectors_0.42.1
## [31] BiocGenerics_0.50.0 EnhancedVolcano_1.22.0
## [33] ggrepel_0.9.6 ggplot2_3.5.1
## [35] patchwork_1.3.0 glmGamPoi_1.16.0
## [37] sctransform_0.4.1 Seurat_5.1.0
## [39] SeuratObject_5.0.2 sp_2.1-4
## [41] stringr_1.5.1 dplyr_1.1.4
## [43] knitr_1.48
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.22 splines_4.4.1 later_1.3.2
## [4] cellranger_1.1.0 polyclip_1.10-7 fastDummies_1.7.4
## [7] lifecycle_1.0.4 rprojroot_2.0.4 globals_0.16.3
## [10] lattice_0.22-6 MASS_7.3-61 magrittr_2.0.3
## [13] plotly_4.10.4 sass_0.4.9 rmarkdown_2.28
## [16] jquerylib_0.1.4 yaml_2.3.10 httpuv_1.6.15
## [19] spam_2.10-0 zip_2.3.1 spatstat.sparse_3.1-0
## [22] reticulate_1.39.0 cowplot_1.1.3 pbapply_1.7-2
## [25] abind_1.4-8 zlibbioc_1.50.0 Rtsne_0.17
## [28] GenomeInfoDbData_1.2.12 irlba_2.3.5.1 listenv_0.9.1
## [31] spatstat.utils_3.1-0 MatrixModels_0.5-3 goftest_1.2-3
## [34] RSpectra_0.16-2 spatstat.random_3.3-2 fitdistrplus_1.2-1
## [37] parallelly_1.38.0 leiden_0.4.3.1 codetools_0.2-20
## [40] DelayedArray_0.30.1 xml2_1.3.6 tidyselect_1.2.1
## [43] UCSC.utils_1.0.0 farver_2.1.2 spatstat.explore_3.3-2
## [46] jsonlite_1.8.8 progressr_0.14.0 ggridges_0.5.6
## [49] survival_3.7-0 systemfonts_1.1.0 tools_4.4.1
## [52] ragg_1.3.3 ica_1.0-3 Rcpp_1.0.13
## [55] glue_1.7.0 gridExtra_2.3 SparseArray_1.4.8
## [58] xfun_0.47 withr_3.0.1 BiocManager_1.30.25
## [61] fastmap_1.2.0 fansi_1.0.6 SparseM_1.84-2
## [64] digest_0.6.37 timechange_0.3.0 R6_2.5.1
## [67] mime_0.12 textshaping_0.4.0 colorspace_2.1-1
## [70] scattermore_1.2 tensor_1.5 spatstat.data_3.1-2
## [73] utf8_1.2.4 generics_0.1.3 renv_1.0.7
## [76] httr_1.4.7 htmlwidgets_1.6.4 S4Arrays_1.4.1
## [79] uwot_0.2.2 pkgconfig_2.0.3 gtable_0.3.5
## [82] lmtest_0.9-40 XVector_0.44.0 htmltools_0.5.8.1
## [85] dotCall64_1.1-1 scales_1.3.0 png_0.1-8
## [88] spatstat.univar_3.0-1 rstudioapi_0.16.0 tzdb_0.4.0
## [91] reshape2_1.4.4 nlme_3.1-166 cachem_1.1.0
## [94] zoo_1.8-12 KernSmooth_2.23-24 parallel_4.4.1
## [97] miniUI_0.1.1.1 pillar_1.9.0 vctrs_0.6.5
## [100] RANN_2.6.2 promises_1.3.0 xtable_1.8-4
## [103] cluster_2.1.6 evaluate_1.0.0 cli_3.6.3
## [106] locfit_1.5-9.10 compiler_4.4.1 rlang_1.1.4
## [109] crayon_1.5.3 future.apply_1.11.2 labeling_0.4.3
## [112] plyr_1.8.9 stringi_1.8.4 viridisLite_0.4.2
## [115] deldir_2.0-4 BiocParallel_1.38.0 munsell_0.5.1
## [118] lazyeval_0.2.2 spatstat.geom_3.3-3 quantreg_5.98
## [121] Matrix_1.7-0 RcppHNSW_0.6.0 hms_1.1.3
## [124] future_1.34.0 shiny_1.9.1 highr_0.11
## [127] ROCR_1.0-11 igraph_2.0.3 bslib_0.8.0
## [130] polynom_1.4-1